{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow2教程-Keras函数式API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数API是一种创建模型的方式，该方法比Sequential以下方法更加灵活：它可以处理具有非线性拓扑的模型，具有共享层的模型以及具有多个输入或输出的模型。\n",
    "\n",
    "它基于以下思想：深度学习模型通常是层的有向无环图（DAG）。Functional API是一组用于构建层图的工具。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pydot\n",
    "#!sudo apt-get install graphvizf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/doit/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.layers as layers\n",
    "tf.keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 构建简单的网络\n",
    "### 1.1 创建网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mnist_model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "img (InputLayer)             [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                25120     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                1056      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                330       \n",
      "=================================================================\n",
      "Total params: 26,506\n",
      "Trainable params: 26,506\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "inputs = tf.keras.Input(shape=(784,), name='img')\n",
    "# 以上一层的输出作为下一层的输入\n",
    "h1 = layers.Dense(32, activation='relu')(inputs)\n",
    "h2 = layers.Dense(32, activation='relu')(h1)\n",
    "outputs = layers.Dense(10, activation='softmax')(h2)\n",
    "model = tf.keras.Model(inputs=inputs, outputs=outputs, name='mnist_model')  # 名字字符串中不能有空格\n",
    "\n",
    "model.summary()\n",
    "keras.utils.plot_model(model, 'mnist_model.png')\n",
    "keras.utils.plot_model(model, 'model_info.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“层图”是用于深度学习模型的非常直观的结构图，而函数式API是构建结构图对应模型的方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 训练、验证及测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/5\n",
      "48000/48000 [==============================] - 3s 53us/sample - loss: 0.4210 - accuracy: 0.8833 - val_loss: 0.2289 - val_accuracy: 0.9322\n",
      "Epoch 2/5\n",
      "48000/48000 [==============================] - 1s 27us/sample - loss: 0.2056 - accuracy: 0.9399 - val_loss: 0.1690 - val_accuracy: 0.9501\n",
      "Epoch 3/5\n",
      "48000/48000 [==============================] - 1s 27us/sample - loss: 0.1633 - accuracy: 0.9516 - val_loss: 0.1466 - val_accuracy: 0.9568\n",
      "Epoch 4/5\n",
      "48000/48000 [==============================] - 1s 27us/sample - loss: 0.1403 - accuracy: 0.9585 - val_loss: 0.1343 - val_accuracy: 0.9594\n",
      "Epoch 5/5\n",
      "48000/48000 [==============================] - 1s 27us/sample - loss: 0.1235 - accuracy: 0.9638 - val_loss: 0.1311 - val_accuracy: 0.9579\n",
      "test loss: 0.13712323768194765\n",
      "test acc: 0.959\n"
     ]
    }
   ],
   "source": [
    "# 模型的训练、验证和测试与训练模型完全相同\n",
    "# 下面使用mnist数据集进行展示\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
    "# 将数值归到0-1之间\n",
    "x_train = x_train.reshape(60000, 784).astype('float32') /255\n",
    "x_test = x_test.reshape(10000, 784).astype('float32') /255\n",
    "model.compile(optimizer=keras.optimizers.RMSprop(),\n",
    "             loss='sparse_categorical_crossentropy', # 直接填api，后面会报错\n",
    "             metrics=['accuracy'])\n",
    "history = model.fit(x_train, y_train, batch_size=64, epochs=5, validation_split=0.2)\n",
    "test_scores = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('test loss:', test_scores[0])\n",
    "print('test acc:', test_scores[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 模型保存和序列化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型的保存与序列化与Sequential模型完全一样\n",
    "model.save('model_save.h5')\n",
    "del model\n",
    "model = keras.models.load_model('model_save.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 使用共享网络创建多个模型\n",
    "在函数式API中，通过在图层网络中指定其输入和输出来创建模型。 这意味着可以使用单个图层图来生成多个模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "img (InputLayer)             [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 26, 26, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624      \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d (Global (None, 16)                0         \n",
      "=================================================================\n",
      "Total params: 18,672\n",
      "Trainable params: 18,672\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "img (InputLayer)             [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 26, 26, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 16)          4624      \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d (Global (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 4, 4, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 6, 6, 16)          160       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "up_sampling2d (UpSampling2D) (None, 24, 24, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16)        4624      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 28,241\n",
      "Trainable params: 28,241\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 自编码器网络结构\n",
    "# 编码器\n",
    "encode_input = keras.Input(shape=(28,28,1), name='img')\n",
    "h1 = layers.Conv2D(16, 3, activation='relu')(encode_input)\n",
    "h1 = layers.Conv2D(32, 3, activation='relu')(h1)\n",
    "h1 = layers.MaxPool2D(3)(h1)\n",
    "h1 = layers.Conv2D(32, 3, activation='relu')(h1)\n",
    "h1 = layers.Conv2D(16, 3, activation='relu')(h1)\n",
    "encode_output = layers.GlobalMaxPool2D()(h1)\n",
    "\n",
    "encode_model = keras.Model(inputs=encode_input, outputs=encode_output, name='encoder')\n",
    "encode_model.summary()\n",
    "# 解码器\n",
    "h2 = layers.Reshape((4, 4, 1))(encode_output)\n",
    "h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\n",
    "h2 = layers.Conv2DTranspose(32, 3, activation='relu')(h2)\n",
    "h2 = layers.UpSampling2D(3)(h2)\n",
    "h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\n",
    "decode_output = layers.Conv2DTranspose(1, 3, activation='relu')(h2)\n",
    "\n",
    "autoencoder = keras.Model(inputs=encode_input, outputs=decode_output, name='autoencoder')\n",
    "autoencoder.summary()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，我们使解码架构与编码架构严格对称，因此我们得到的输出形状与输入形状相同(28, 28, 1)。Conv2D一层的反面是Conv2DTranspose一层，MaxPooling2D一层的反面是UpSampling2D一层。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可以把整个模型，当作一层网络使用**\n",
    "我们可以通过在另一层的Input或Output上调用任何模型，将其视为层。请注意，通过调用模型，我们不仅可以重用模型的体系结构，还可以重用其权重。\n",
    "\n",
    "下面是对自动编码器示例的另一种处理方式，该示例创建一个编码器模型，一个解码器模型，并将它们链接到两个调用中以获得自编码器模型：\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "src_img (InputLayer)         [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 26, 26, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 24, 24, 32)        4640      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 4, 4, 16)          4624      \n",
      "_________________________________________________________________\n",
      "global_max_pooling2d_1 (Glob (None, 16)                0         \n",
      "=================================================================\n",
      "Total params: 18,672\n",
      "Trainable params: 18,672\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoded_img (InputLayer)     [(None, 16)]              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 4, 4, 1)           0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 6, 6, 16)          160       \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTr (None, 8, 8, 32)          4640      \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 24, 24, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTr (None, 26, 26, 16)        4624      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_7 (Conv2DTr (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 9,569\n",
      "Trainable params: 9,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "img (InputLayer)             [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "encoder (Model)              (None, 16)                18672     \n",
      "_________________________________________________________________\n",
      "decoder (Model)              (None, 28, 28, 1)         9569      \n",
      "=================================================================\n",
      "Total params: 28,241\n",
      "Trainable params: 28,241\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encode_input = keras.Input(shape=(28,28,1), name='src_img')\n",
    "h1 = layers.Conv2D(16, 3, activation='relu')(encode_input)\n",
    "h1 = layers.Conv2D(32, 3, activation='relu')(h1)\n",
    "h1 = layers.MaxPool2D(3)(h1)\n",
    "h1 = layers.Conv2D(32, 3, activation='relu')(h1)\n",
    "h1 = layers.Conv2D(16, 3, activation='relu')(h1)\n",
    "encode_output = layers.GlobalMaxPool2D()(h1)\n",
    "\n",
    "encode_model = keras.Model(inputs=encode_input, outputs=encode_output, name='encoder')\n",
    "encode_model.summary()\n",
    "\n",
    "decode_input = keras.Input(shape=(16,), name='encoded_img')\n",
    "h2 = layers.Reshape((4, 4, 1))(decode_input)\n",
    "h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\n",
    "h2 = layers.Conv2DTranspose(32, 3, activation='relu')(h2)\n",
    "h2 = layers.UpSampling2D(3)(h2)\n",
    "h2 = layers.Conv2DTranspose(16, 3, activation='relu')(h2)\n",
    "decode_output = layers.Conv2DTranspose(1, 3, activation='relu')(h2)\n",
    "decode_model = keras.Model(inputs=decode_input, outputs=decode_output, name='decoder')\n",
    "decode_model.summary()\n",
    "\n",
    "autoencoder_input = keras.Input(shape=(28,28,1), name='img')\n",
    "h3 = encode_model(autoencoder_input)\n",
    "autoencoder_output = decode_model(h3)\n",
    "autoencoder = keras.Model(inputs=autoencoder_input, outputs=autoencoder_output,\n",
    "                          name='autoencoder')\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型可以嵌套：模型可以包含子模型（因为模型就像一层一样）。\n",
    "\n",
    "**模型集成**\n",
    "\n",
    "模型嵌套的另一种常见模式是集成。以下是将一组模型整合为一个平均其预测值的模型的方法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    inputs = keras.Input(shape=(128,))\n",
    "    outputs = keras.layers.Dense(1, activation='sigmoid')(inputs)\n",
    "    return keras.Model(inputs, outputs)\n",
    "\n",
    "model1 = get_model()\n",
    "model2 = get_model()\n",
    "model3 = get_model()\n",
    "inputs = keras.Input(shape=(128,))\n",
    "y1 = model1(inputs)\n",
    "y2 = model2(inputs)\n",
    "y3 = model3(inputs)\n",
    "outputs = layers.average([y1, y2, y3])\n",
    "ensemble_model = keras.Model(inputs, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 复杂网络结构构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 多输入与多输出网络\n",
    "假设我们正在建立一个系统，用于按优先级对定制票进行排序并将其分配到正确的部门。\n",
    "\n",
    "模型将具有3个输入：\n",
    "\n",
    "- 票证标题（文本输入）\n",
    "- 票证的文本正文（文本输入）\n",
    "- 用户添加的所有标签（分类输入）\n",
    "它将有两个输出：\n",
    "\n",
    "- 优先级得分，介于0和1之间（标量S型输出）\n",
    "- 应该处理票证的部门（softmax输出）\n",
    "仅使用几行Functional API构建该模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "title (InputLayer)              [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "body (InputLayer)               [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, None, 64)     128000      title[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Embedding)           (None, None, 64)     128000      body[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "lstm_1 (LSTM)                   (None, 128)          98816       embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "lstm (LSTM)                     (None, 32)           12416       embedding[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "tag (InputLayer)                [(None, 12)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 172)          0           lstm_1[0][0]                     \n",
      "                                                                 lstm[0][0]                       \n",
      "                                                                 tag[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "priority (Dense)                (None, 1)            173         concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "department (Dense)              (None, 4)            692         concatenate[0][0]                \n",
      "==================================================================================================\n",
      "Total params: 368,097\n",
      "Trainable params: 368,097\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建一个根据定制票标题、内容和标签，预测票证优先级和执行部门的网络\n",
    "# 超参\n",
    "num_words = 2000\n",
    "num_tags = 12\n",
    "num_departments = 4\n",
    "\n",
    "# 输入\n",
    "body_input = keras.Input(shape=(None,), name='body')\n",
    "title_input = keras.Input(shape=(None,), name='title')\n",
    "tag_input = keras.Input(shape=(num_tags,), name='tag')\n",
    "\n",
    "# 嵌入层\n",
    "body_feat = layers.Embedding(num_words, 64)(body_input)\n",
    "title_feat = layers.Embedding(num_words, 64)(title_input)\n",
    "\n",
    "# 特征提取层\n",
    "body_feat = layers.LSTM(32)(body_feat)\n",
    "title_feat = layers.LSTM(128)(title_feat)\n",
    "features = layers.concatenate([title_feat,body_feat, tag_input])\n",
    "\n",
    "# 分类层\n",
    "priority_pred = layers.Dense(1, activation='sigmoid', name='priority')(features)\n",
    "department_pred = layers.Dense(num_departments, activation='softmax', name='department')(features)\n",
    "\n",
    "\n",
    "# 构建模型\n",
    "model = keras.Model(inputs=[body_input, title_input, tag_input],\n",
    "                    outputs=[priority_pred, department_pred])\n",
    "model.summary()\n",
    "keras.utils.plot_model(model, 'multi_model.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "编译此模型时，我们可以为每个输出分配不同的loss。甚至可以为每个loss分配不同的权重，以调整它们对总训练loss的贡献。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
    "             loss={'priority': 'binary_crossentropy',\n",
    "                  'department': 'categorical_crossentropy'},\n",
    "             loss_weights=[1., 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1280 samples\n",
      "Epoch 1/5\n",
      "1280/1280 [==============================] - 6s 5ms/sample - loss: 1.2873 - priority_loss: 0.7099 - department_loss: 2.8868\n",
      "Epoch 2/5\n",
      "1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2777 - priority_loss: 0.6978 - department_loss: 2.8995\n",
      "Epoch 3/5\n",
      "1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2747 - priority_loss: 0.7001 - department_loss: 2.8730\n",
      "Epoch 4/5\n",
      "1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2660 - priority_loss: 0.6968 - department_loss: 2.8459\n",
      "Epoch 5/5\n",
      "1280/1280 [==============================] - 1s 1ms/sample - loss: 1.2594 - priority_loss: 0.6972 - department_loss: 2.8109\n"
     ]
    }
   ],
   "source": [
    "# 构造数据并训练\n",
    "\n",
    "import numpy as np\n",
    "# 载入输入数据\n",
    "title_data = np.random.randint(num_words, size=(1280, 10))\n",
    "body_data = np.random.randint(num_words, size=(1280, 100))\n",
    "tag_data = np.random.randint(2, size=(1280, num_tags)).astype('float32')\n",
    "# 标签\n",
    "priority_label = np.random.random(size=(1280, 1))\n",
    "department_label = np.random.randint(2, size=(1280, num_departments))\n",
    "# 训练\n",
    "history = model.fit(\n",
    "    {'title': title_data, 'body':body_data, 'tag':tag_data},\n",
    "    {'priority':priority_label, 'department':department_label},\n",
    "    batch_size=32,\n",
    "    epochs=5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果使用Dataset构建输入，它应产生一个列表元组（如）([title_data, body_data, tags_data], [priority_targets, dept_targets]) 或一个字典元组（如） ({'title': title_data, 'body': body_data, 'tags': tags_data}, {'priority': priority_targets, 'department': dept_targets})。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 小型残差网络\n",
    "Functional API还可以使操作非线性连接拓扑变得容易，也就是说，模型中的层不是顺序连接。\n",
    "\n",
    "常见的用例是残余连接。\n",
    "\n",
    "下面，我们为CIFAR10建立一个玩具ResNet模型来演示这一点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"small_resnet\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img (InputLayer)                [(None, 32, 32, 3)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 30, 30, 32)   896         img[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 28, 28, 64)   18496       conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 9, 9, 64)     0           conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 9, 9, 64)     36928       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 9, 9, 64)     36928       conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 9, 9, 64)     0           conv2d_11[0][0]                  \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 9, 9, 64)     36928       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 9, 9, 64)     36928       conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 9, 9, 64)     0           conv2d_13[0][0]                  \n",
      "                                                                 add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 7, 7, 64)     36928       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling2d_2 (GlobalM (None, 64)           0           conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 256)          16640       global_max_pooling2d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_7 (Dense)                 (None, 10)           2570        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 223,242\n",
      "Trainable params: 223,242\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = keras.Input(shape=(32,32,3), name='img')\n",
    "h1 = layers.Conv2D(32, 3, activation='relu')(inputs)\n",
    "h1 = layers.Conv2D(64, 3, activation='relu')(h1)\n",
    "block1_out = layers.MaxPooling2D(3)(h1)\n",
    "\n",
    "h2 = layers.Conv2D(64, 3, activation='relu', padding='same')(block1_out)\n",
    "h2 = layers.Conv2D(64, 3, activation='relu', padding='same')(h2)\n",
    "block2_out = layers.add([h2, block1_out])  # 残差连接\n",
    "\n",
    "h3 = layers.Conv2D(64, 3, activation='relu', padding='same')(block2_out)\n",
    "h3 = layers.Conv2D(64, 3, activation='relu', padding='same')(h3)\n",
    "block3_out = layers.add([h3, block2_out])\n",
    "\n",
    "h4 = layers.Conv2D(64, 3, activation='relu')(block3_out)\n",
    "h4 = layers.GlobalMaxPool2D()(h4)\n",
    "h4 = layers.Dense(256, activation='relu')(h4)\n",
    "h4 = layers.Dropout(0.5)(h4)\n",
    "outputs = layers.Dense(10, activation='softmax')(h4)\n",
    "\n",
    "model = keras.Model(inputs, outputs, name='small_resnet')  # 网络名不能有空格\n",
    "model.summary()\n",
    "keras.utils.plot_model(model, 'small_resnet_model.png', show_shapes=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练残差网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 40000 samples, validate on 10000 samples\n",
      "40000/40000 [==============================] - 91s 2ms/sample - loss: 1.8759 - acc: 0.3005 - val_loss: 1.5772 - val_acc: 0.4288\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fba5d711860>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = y_train.astype('float32') / 255\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)\n",
    "\n",
    "model.compile(optimizer=keras.optimizers.RMSprop(1e-3),\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['acc'])\n",
    "model.fit(x_train, y_train,\n",
    "         batch_size=64,\n",
    "         epochs=1,\n",
    "         validation_split=0.2)\n",
    "\n",
    "#model.predict(x_test, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 共享网络层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "函数式API的另一个优点是：可以使用共享层的模型。共享层是在同一模型中多次重用的层实例：它们学习与层图中的多个路径相对应的要素。\n",
    "\n",
    "共享层通常用于编码来自相似空间的输入（例如，两个具有相似词汇的不同文本），因为它们可以在这些不同输入之间共享信息，并且可以在更少的空间上训练这种模型数据。\n",
    "\n",
    "要在函数式API中共享图层，只需多次调用同一图层实例即可。例如，这是一个跨两个不同文本输入的共享Embedding图层："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "share_embedding = layers.Embedding(1000, 64)\n",
    "\n",
    "input1 = keras.Input(shape=(None,), dtype='int32')\n",
    "input2 = keras.Input(shape=(None,), dtype='int32')\n",
    "\n",
    "feat1 = share_embedding(input1)\n",
    "feat2 = share_embedding(input2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 模型复用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于函数式API中要处理的层图是静态数据结构，因此可以对其进行访问和检查。\n",
    "\n",
    "这也意味着我们可以访问中间层（图中的“节点”）的输出，并在其他地方重用它们。这对于特征提取非常有用！\n",
    "\n",
    "下面的一个例子是VGG16模型，其权重在ImageNet上进行了预训练："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "vgg16=VGG16()\n",
    "# 获取中间结构输出\n",
    "feature_list = [layer.output for layer in vgg16.layers]\n",
    "# 将其作为新模型输出\n",
    "feat_ext_model = keras.Model(inputs=vgg16.input, outputs=feature_list)\n",
    "\n",
    "img = np.random.random((1, 224, 224, 3)).astype('float32')\n",
    "# 用于提取特征\n",
    "ext_features = feat_ext_model(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 自定义网络层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tf.keras具有广泛的内置层。这里有一些例子：\n",
    "\n",
    "- 卷积层：Conv1D，Conv2D，Conv3D，Conv2DTranspose，等。\n",
    "- 池层：MaxPooling1D，MaxPooling2D，MaxPooling3D，AveragePooling1D，等。\n",
    "- RNN层：GRU，LSTM，ConvLSTM2D，等。\n",
    "- BatchNormalization，Dropout，Embedding，等。\n",
    "如果找不到所需的内容，则可以通过创建自己的图层来扩展API。\n",
    "\n",
    "所有层都对该Layer类进行子类化并实现：\n",
    "- 一个call方法，指定由该层完成的计算。\n",
    "- 一种build创建图层权重的方法（请注意，这只是一种样式约定；也可以在__init__函数中创建权重）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_8 (InputLayer)         [(None, 4)]               0         \n",
      "_________________________________________________________________\n",
      "my_dense_1 (MyDense)         (None, 10)                50        \n",
      "=================================================================\n",
      "Total params: 50\n",
      "Trainable params: 50\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import tensorflow as tf\n",
    "# import tensorflow.keras as keras\n",
    "class MyDense(layers.Layer):\n",
    "    def __init__(self, units=32):\n",
    "        super(MyDense, self).__init__()\n",
    "        self.units = units\n",
    "    def build(self, input_shape):\n",
    "        # 构建权重\n",
    "        self.w = self.add_weight(shape=(input_shape[-1], self.units),\n",
    "                                 initializer='random_normal',\n",
    "                                 trainable=True)\n",
    "        self.b = self.add_weight(shape=(self.units,),\n",
    "                                 initializer='random_normal',\n",
    "                                 trainable=True)\n",
    "    def call(self, inputs):\n",
    "        # 正向传播\n",
    "        return tf.matmul(inputs, self.w) + self.b\n",
    "    \n",
    "    def get_config(self):\n",
    "        # 支持序列化\n",
    "        return {'units': self.units}\n",
    "# 构建模型\n",
    "inputs = keras.Input((4,))\n",
    "outputs = MyDense(10)(inputs)\n",
    "model = keras.Model(inputs, outputs)\n",
    "\n",
    "\n",
    "# 模型序列化\n",
    "config = model.get_config()\n",
    "new_model = keras.Model.from_config(\n",
    "config, custom_objects={'MyDense':MyDense}\n",
    ")\n",
    "new_model.summary()\n",
    "keras.utils.plot_model(new_model, 'myDense.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**在自定义网络层调用其他网络层**\n",
    "\n",
    "构建一个rnn网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 8, 32)\n",
      "(32, 8, 32)\n",
      "Model: \"model_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_9 (InputLayer)         [(32, 10, 5)]             0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (32, 8, 32)               512       \n",
      "_________________________________________________________________\n",
      "my_rnn (MyRnn)               (32, 8, 1)                2145      \n",
      "=================================================================\n",
      "Total params: 2,657\n",
      "Trainable params: 2,657\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 超参\n",
    "time_step = 10\n",
    "batch_size = 32\n",
    "hidden_dim = 32\n",
    "inputs_dim = 5\n",
    "\n",
    "# 网络\n",
    "class MyRnn(layers.Layer):\n",
    "    def __init__(self):\n",
    "        super(MyRnn, self).__init__()\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.projection1 = layers.Dense(units=hidden_dim, activation='tanh')\n",
    "        self.projection2 = layers.Dense(units=hidden_dim, activation='tanh')\n",
    "        self.classifier = layers.Dense(1, activation='sigmoid')\n",
    "    def call(self, inputs):\n",
    "        outs = []\n",
    "        states = tf.zeros(shape=[inputs.shape[0], self.hidden_dim])\n",
    "        for t in range(inputs.shape[1]):\n",
    "            x = inputs[:,t,:]\n",
    "            h = self.projection1(x)\n",
    "            y = h + self.projection2(states)\n",
    "            states = y\n",
    "            outs.append(y)\n",
    "        # print(outs)\n",
    "        features = tf.stack(outs, axis=1)\n",
    "        print(features.shape)\n",
    "        return self.classifier(features)\n",
    "\n",
    "# 构建网络\n",
    "inputs = keras.Input(batch_shape=(batch_size, time_step, inputs_dim))\n",
    "x = layers.Conv1D(32, 3)(inputs)\n",
    "print(x.shape)\n",
    "outputs = MyRnn()(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.summary()\n",
    "keras.utils.plot_model(model, 'myRnn.png', show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 32)\n"
     ]
    }
   ],
   "source": [
    "rnn_model = MyRnn()\n",
    "_ = rnn_model(tf.zeros((1, 10, 5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7 何时使用函数式API\n",
    "如何决定是使用函数式API创建新模型，还是Model直接对类进行子类化？\n",
    "\n",
    "通常，函数式API是更高级别的，更易于使用和更安全的构建方法，并且具有许多子类化模型不支持的功能。\n",
    "\n",
    "但是，在创建不容易表示为有向无环图的层的模型时，模型子类化为您提供了更大的灵活性（例如，您无法使用Functional API实现Tree-RNN，您必须Model直接子类化）。\n",
    "\n",
    "**功能性API的优点如下：**\n",
    "\n",
    "下面列出的属性对于顺序模型（也是数据结构）也都是正确的，但对于子类模型（它们是Python字节码，不是数据结构）则不是正确的。\n",
    "\n",
    "- 它不那么冗长。不需要\\__init\\__函数和call函数。\n",
    "- 在定义模型时，它将验证模型。\n",
    "    - 在Functional API中，输入规范（shape和dtype）是预先创建的（通过Input），并且每次调用图层时，该图层都会检查传递给它的规范是否符合其假设。\n",
    "    - 这样可以保证使用Functional API构建的任何模型都可以运行。所有调试（与收敛相关的调试除外）将在模型构建过程中静态发生，而不是在执行时发生。这类似于在编译器中进行类型检查。\n",
    "- 函数式模型是可绘制且可检查的。\n",
    "    - 可以将模型绘制为图形，并且可以轻松访问该图形中的中间节点-例如，以提取和重用中间层的输出。\n",
    "- 函数式模型模型可以序列化或克隆\n",
    "    - 因为函数式模型是数据结构而不是一段代码，所以它可以安全地序列化，并且可以保存为单个文件，可以重新创建完全相同的模型，而无需访问任何原始代码。\n",
    "\n",
    "**功能性API的缺点如下：**\n",
    "- 它不支持动态架构。\n",
    "    - Functional API将模型视为层的DAG。对于大多数深度学习架构（但不是全部），这是正确的：但是，递归网络或Tree RNN不遵循此假设，并且无法在Functional API中实现。\n",
    "\n",
    "- 有时，需要从头开始编写所有内容。\n",
    "    - 在编写高级体系结构时，可能想做“定义层的DAG”范围之外的事情：例如，可能需要在模型实例上公开多个自定义训练和推理方法。这需要子类化。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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